code: def prepare(self, priming_data):
      os.environ["TOKENIZERS_PARALLELISM"] = "true"
      self._max_len = max([len(x) for x in priming_data])
      self._tokenizer = self._tokenizer_class.from_pretrained(
          self._pretrained_model_name
      )
      self._pad_id = self._tokenizer.convert_tokens_to_ids(
          [self._tokenizer.pad_token]
      )[0]
  
file_path: lightwood/lightwood/encoder/text/vocab.py
imports: import os
  import torch
  from transformers import DistilBertTokenizer
  from lightwood.encoder.base import BaseEncoder
label_desc: Use image node_id 2 for calling the VocabularyEncoder obj's underlying member method code with example usage: obj.prepare(priming_data) without return types